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Record W4281711780 · doi:10.26504/bp202301

The impact of Irish budgetary policy by disability status

2022· report· en· W4281711780 on OpenAlexaff
Karina Doorley, Mark Regan

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsTrinity College
FundersJoint Research CentreEuropean Commission
KeywordsWelfareEconomicsDisability benefitsPovertyIrishStandard of livingPopulationDemographic economicsWageLabour economicsEconomic growthMedicineSocial security

Abstract

fetched live from OpenAlex

Existing research has shown that disability is costly and can result in an increased risk of living in poverty and a decrease in living standards. In this paper, we expand a framework of equality budgeting, previously applied from a gender perspective, to the population of households affected by disability. Using a microsimulation model linked to data from the EU Survey of Income and Living Conditions (EU-SILC), we show how tax-benefit policy and other market income changes between 2007 and 2019 impacted households affected by disability and households not affected by disability. We find that disposable (or post-tax and transfer) income grew for both types of households but at a faster rate for households affected by disability than households not affected by disability. This income growth was driven by two counteracting forces. On the one hand, tax and welfare policy failed to keep pace with market income growth, reducing the living standards of households affected by disability by more than households not affected by disability. On the other hand, despite having lower average wage levels, wage growth for workers affected by disability outpaced wage growth for workers not affected by disability, while the labour supply of households affected by disability also increased. Future attempts to equality-proof budgetary policy should consider that changes to welfare disproportionally affect households with disabilities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.213
GPT teacher head0.506
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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